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Article(id=1241759333978017795, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241759317016252418, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202404549, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1714406400000, receivedDateStr=2024-04-30, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773988095966, onlineDateStr=2026-03-20, pubDate=1727193600000, pubDateStr=2024-09-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773988095966, onlineIssueDateStr=2026-03-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773988095966, creator=13701087609, updateTime=1773988095966, updator=13701087609, issue=Issue{id=1241759317016252418, tenantId=1146029695717560320, journalId=1227665162245664772, year='2024', volume='51', issue='18', pageStart='3265', pageEnd='3456', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773988091898, creator=13701087609, updateTime=1773991617194, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241774103024174010, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241759317016252418, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241774103024174011, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241759317016252418, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=3289, endPage=3294, ext={EN=ArticleExt(id=1241759335647350809, articleId=1241759333978017795, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=A study of obesity class prediction built on neural networks, columnId=null, journalTitle=Modern Preventive Medicine, columnName=null, runingTitle=null, highlight=null, articleAbstract=
Objective

To use neural networks and optimization algorithms, establish an obesity level prediction model to assess obesity risk.

Methods

Perform correlation analysis on 2 111 recorded data collected from participants aged between 14 and 61 years old in Mexico, Peru, and Colombia, and establish a BP neural network obesity level prediction model. At the same time, optimize the number of hidden nodes and transfer function of the model through pruning to find the optimal network structure. In addition, the genetic algorithm and the simulated annealing algorithm were used to optimize the weights and thresholds of the model, ultimately establishing a high-precision and practical GASA-BP neural network obesity level prediction model.

Results

The R2 of the prediction model was 0.975 1, and the MAE was 0.352, indicating high prediction accuracy and strong practicality. In the process of predicting obesity levels in the model, weight index was the most important, with a correlation of 0.913 with obesity levels. The correlation between overweight members in the family was also relatively strong, with a correlation of 0.505.

Conclusion

The GASA-BP neural network prediction model performs better than other models in predicting obesity levels, and can make the most accurate prediction of obesity levels, providing guidance and reference for personalized obesity assessments and subsequent prevention and control measures.

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目的

使用神经网络以及优化算法，建立肥胖等级预测模型，对肥胖风险进行评估。

方法

对从墨西哥、秘鲁和哥伦比亚收集的年龄在14岁至61岁之间参与者的2 111条记录数据进行相关性分析并建立BP神经网络肥胖等级预测模型，同时通过剪枝法对模型的隐层节点数和传递函数进行优选，找出最优网络结构。另外利用遗传算法和模拟退火算法对模型权值和阈值进行优化，最终建立起精确度高、实用性强的GASA-BP神经网络肥胖等级预测模型。

结果

预测模型预测结果的R2为0.975 1，MAE为0.352，预测精度高，实用性强。在模型预测肥胖等级过程中，体重指标最为重要，与肥胖等级相关性达到了0.913，家族中是否有超重成员相关性也比较强，相关性为0.505。

结论

GASA-BP神经网络预测模型在预测肥胖等级方面性能优于其他模型，能够对肥胖等级做出最为准确的预测，可为个性化肥胖评估以及后续防控措施的制定提供一定的指导和参考。

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王强芬, E-mail:
, copyrightStatement=本刊刊出的所有文章不代表中华预防医学会和本刊编委会的观点，除非特别声明。, copyrightOwner=中华预防医学会和四川大学华西公共卫生学院, extLink=null, articleAbsUrl=null, sourceXml=1YWGRE09CbKtflgFtDCcYQ==, magXml=IpLideYEj0kFK1RVC8S1qw==, pdfUrl=null, pdf=3PGBIJ2frq+TQlCoZ7Yeuw==, pdfFileSize=1389040, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=Hd41dHwNGgGHh/SrJ+9wrA==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=PjYv3IS8zpvZvWLb6NlKUg==, mapNumber=null, authorCompany=null, fund=null, authors=

秦晓静(1994—)，女, 硕士在读, 研究方向:人工智能在公共卫生等领域的应用

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figs=[ArticleFig(id=1241759365087171308, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759333978017795, language=EN, label=Fig.1, caption=Overall framework of the model, figureFileSmall=AwcVlBYpt+n1w7WFXKMkmA==, figureFileBig=Hd41dHwNGgGHh/SrJ+9wrA==, tableContent=null), ArticleFig(id=1241759367083660023, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759333978017795, language=CN, label=图1, caption=研究整体框架, figureFileSmall=AwcVlBYpt+n1w7WFXKMkmA==, figureFileBig=Hd41dHwNGgGHh/SrJ+9wrA==, tableContent=null), ArticleFig(id=1241759368006406952, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759333978017795, language=EN, label=Fig.2, caption=Correlation coefficient matrix of influencing factors of obesity level, figureFileSmall=upJ2tDEDYE9T7kPPOrKPqQ==, figureFileBig=eIIVfUfqNz4IZZidrCBV3Q==, tableContent=null), ArticleFig(id=1241759368211927861, tenantId=1146029695717560320, journalId=1227665162245664772, 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Weight obesity level

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肥胖等级BMI范围
体重不足<18.5
正常18.5~24.9
超重25~29.9
肥胖I30~34.9
肥胖II35~39.9
肥胖III>40
), ArticleFig(id=1241759369898038166, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759333978017795, language=CN, label=表1, caption=

体重肥胖等级

, figureFileSmall=null, figureFileBig=null, tableContent=
肥胖等级BMI范围
体重不足<18.5
正常18.5~24.9
超重25~29.9
肥胖I30~34.9
肥胖II35~39.9
肥胖III>40
), ArticleFig(id=1241759370200028068, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759333978017795, language=EN, label=Table 2, caption=

Variable assignment

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类型代号
性别1=女性，2=男性
家庭成员是否超重1=否，2=是
是否抽烟1=否，2=是
是否监测每天摄入卡路里1=是，2=否
是否经常食用高热量食物1=否，2=是
两餐之间进食情况1=从不，2=有时，3=经常，4=一直
饮酒情况1=从不，2=有时，3=经常
出行方式1=步行，2=公共交通，3=自行车，4=摩托车，5=汽车
肥胖等级1=体重不足，2=正常体重，3=1级超重，4=2级超重，5=1级肥胖，6=2级肥胖，7=3级肥胖
), ArticleFig(id=1241759370627847091, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759333978017795, language=CN, label=表2, caption=

变量赋值

, figureFileSmall=null, figureFileBig=null, tableContent=
类型代号
性别1=女性，2=男性
家庭成员是否超重1=否，2=是
是否抽烟1=否，2=是
是否监测每天摄入卡路里1=是，2=否
是否经常食用高热量食物1=否，2=是
两餐之间进食情况1=从不，2=有时，3=经常，4=一直
饮酒情况1=从不，2=有时，3=经常
出行方式1=步行，2=公共交通，3=自行车，4=摩托车，5=汽车
肥胖等级1=体重不足，2=正常体重，3=1级超重，4=2级超重，5=1级肥胖，6=2级肥胖，7=3级肥胖
), ArticleFig(id=1241759371131163583, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759333978017795, language=EN, label=Table 3, caption=

Prediction results of different transfer functions

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传递函数purelinlogsigtansig
R20.8900.0780.851
MAE0.7241.6950.825
), ArticleFig(id=1241759371286352838, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759333978017795, language=CN, label=表3, caption=

不同传递函数预测结果

, figureFileSmall=null, figureFileBig=null, tableContent=
传递函数purelinlogsigtansig
R20.8900.0780.851
MAE0.7241.6950.825
), ArticleFig(id=1241759371487679443, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759333978017795, language=EN, label=Table 4, caption=

Analysis of prediction results of different models

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模型名称R2MAE
BP0.8900.724
GA-BP0.9680.404
GASA-BP0.9750.352
ELM0.6521.201
), ArticleFig(id=1241759371697394653, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759333978017795, language=CN, label=表4, caption=

不同预测模型的预测误差及相关系数

, figureFileSmall=null, figureFileBig=null, tableContent=
模型名称R2MAE
BP0.8900.724
GA-BP0.9680.404
GASA-BP0.9750.352
ELM0.6521.201
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基于神经网络的肥胖等级预测研究
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秦晓静 1, 2 , 周蒙 2 , 王强芬 1 , 张鑫 2
现代预防医学 | 流行病与统计方法 2024,51(18): 3289-3294
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现代预防医学 | 流行病与统计方法 2024, 51(18): 3289-3294
基于神经网络的肥胖等级预测研究
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秦晓静1, 2, 周蒙2, 王强芬1 , 张鑫2
作者信息
  • 1.桂林医学院人文与管理学院，广西 桂林 541000
  • 2.青岛市市立医院病案管理科

    • 秦晓静(1994—)，女, 硕士在读, 研究方向:人工智能在公共卫生等领域的应用

通讯作者:

王强芬, E-mail:
A study of obesity class prediction built on neural networks
Xiao-jing QIN1, 2, Meng ZHOU2, Qiang-fen WANG1 , Xin ZHANG2
Affiliations
  • School of Humanities and Management, Guilin Medical University, Guilin, Guangxi 541000, China
出版时间: 2024-09-25 doi: 10.20043/j.cnki.MPM.202404549
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摘要
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目的

使用神经网络以及优化算法，建立肥胖等级预测模型，对肥胖风险进行评估。

方法

对从墨西哥、秘鲁和哥伦比亚收集的年龄在14岁至61岁之间参与者的2 111条记录数据进行相关性分析并建立BP神经网络肥胖等级预测模型，同时通过剪枝法对模型的隐层节点数和传递函数进行优选，找出最优网络结构。另外利用遗传算法和模拟退火算法对模型权值和阈值进行优化，最终建立起精确度高、实用性强的GASA-BP神经网络肥胖等级预测模型。

结果

预测模型预测结果的R2为0.975 1，MAE为0.352，预测精度高，实用性强。在模型预测肥胖等级过程中，体重指标最为重要，与肥胖等级相关性达到了0.913，家族中是否有超重成员相关性也比较强，相关性为0.505。

结论

GASA-BP神经网络预测模型在预测肥胖等级方面性能优于其他模型，能够对肥胖等级做出最为准确的预测，可为个性化肥胖评估以及后续防控措施的制定提供一定的指导和参考。

肥胖预测  /  BP神经网络  /  遗传算法  /  模拟退火算法
Objective

To use neural networks and optimization algorithms, establish an obesity level prediction model to assess obesity risk.

Methods

Perform correlation analysis on 2 111 recorded data collected from participants aged between 14 and 61 years old in Mexico, Peru, and Colombia, and establish a BP neural network obesity level prediction model. At the same time, optimize the number of hidden nodes and transfer function of the model through pruning to find the optimal network structure. In addition, the genetic algorithm and the simulated annealing algorithm were used to optimize the weights and thresholds of the model, ultimately establishing a high-precision and practical GASA-BP neural network obesity level prediction model.

Results

The R2 of the prediction model was 0.975 1, and the MAE was 0.352, indicating high prediction accuracy and strong practicality. In the process of predicting obesity levels in the model, weight index was the most important, with a correlation of 0.913 with obesity levels. The correlation between overweight members in the family was also relatively strong, with a correlation of 0.505.

Conclusion

The GASA-BP neural network prediction model performs better than other models in predicting obesity levels, and can make the most accurate prediction of obesity levels, providing guidance and reference for personalized obesity assessments and subsequent prevention and control measures.

Obesity prediction  /  BP neural network  /  Genetic algorithm  /  Simulated annealing algorithm
秦晓静, 周蒙, 王强芬, 张鑫. 基于神经网络的肥胖等级预测研究. 现代预防医学, 2024 , 51 (18) : 3289 -3294 . DOI: 10.20043/j.cnki.MPM.202404549
Xiao-jing QIN, Meng ZHOU, Qiang-fen WANG, Xin ZHANG. A study of obesity class prediction built on neural networks[J]. Modern Preventive Medicine, 2024 , 51 (18) : 3289 -3294 . DOI: 10.20043/j.cnki.MPM.202404549
正文
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随着生活水平的不断提高，肥胖人群也越来越多，尤其在发展中国家，肥胖和超重人群的增长更为迅速。肥胖不仅对人们的生活造成诸多不便之处，同时也会增加罹患其他疾病的风险[1-2]，因此肥胖问题已经引起了全世界的关注，尤其是在如何对其进行预防与控制等方面。
近年来，利用计算机技术辅助研究肥胖显示出了重要价值[3-4]，尤其是人工智能技术在肥胖分析及预测方面已有一些研究[5-6]。如Lim等[7]建立了10岁儿童肥胖的预测模型，并使用机器学习方法识别相关危险因素；陆晓宇等[8]构建了logistic回归和随机森林等方法来建立超重肥胖风险评估预测模型；王瑾瑾等[9]通过单因素logistic回归分析等方法，建立了代谢型肥胖正常体重发病风险预测模型。虽然许多专家学者对肥胖以及相关影响因素进行了深入分析，但是如何准确预测身体肥胖情况，进而采取相应预防措施的研究较少[10]，而准确预测身体肥胖情况对于降低各种疾病的发生至关重要。根据广泛的文献回顾，基于混合算法的预测模型比单一算法的预测精度更高，如李高伟等人研究发现相比单一的BP神经网络，GA优化后BP神经网络模型的精度提高了58.9%[11]。尹新等人研究发现BP算法、遗传算法等混合算法预测模型可有效地避免了传统的模糊神经网络收敛慢且容易陷入局部最小的缺点[12]。可见这种多算法融合的方法已被现有研究证实，能够显著提高评估模型的预测精度和泛化能力。因此，本研究采用GA、SA和BPNN，开发了一个综合的肥胖程度评估模型，采用GA进行全局参数优化，SA进行局部搜索以增强模型的鲁棒性，以及BPNN来建模肥胖评估的非线性关系。根据每个个体的身体状况及生活习惯等对肥胖等级进行预测，通过算法的协同作用，实现对肥胖程度的精确评估，从而为临床和公共卫生决策提供科学依据，也为个性化医疗提供理论支持，为后续相应的预防措施提供一定的借鉴和指导[13]。
1　资料和方法
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1.1　数据来源及处理
1.1.1　数据来源
本研究数据来源于UCI机器学习数据库[14]。UCI数据库是一个公认的机器学习资源库，主要为机器学习提供数据支持。本研究使用的数据集包含从墨西哥、秘鲁和哥伦比亚收集的年龄在14岁至61岁之间的参与者的2 111条数据，记录了参与者的饮食习惯和身体活动水平等参数，这些数据可用于评估对应的肥胖程度级别[15]，另外肥胖等级分类依据世卫组织计算的身体质量指数（BMI）的分类标准进行，见表1。该数据库还包含与肥胖等级相关的16个属性，分别为与饮食习惯相关的参数，包括是否经常食用高热量食物（FAVC）、食用蔬菜的频率（FCVC）、主餐数量（NCP）、两餐之间的食物消费量（CAEC）、每日用水量（CH2O）和酒精摄入量（CALC）；与身体活动状况相关的参数，包括卡路里消耗量（SCC）、身体活动频率（FAF）、使用技术设备的时间（TUE）、使用的交通工具（MTRANS）；与身体质量相关的参数则包括性别、年龄、身高、体重、家族中是否有超重成员（FH）和吸烟。
1.1.2　变量赋值
由于收集到的一些参数是定性描述，若不处理则无法利用机器学习进行预测，对此本文对饮酒情况、吸烟情况、出行方式等参数进行赋值。见表2。
1.1.3　数据归一化
从表2中可以看出，由于各参数在数量级上存在显著差异，这可能会对预测模型的准确性产生不利影响。为了降低参数数量级差异对模型性能的潜在影响，我们对数据进行了归一化处理[16-17]，将所有参数值缩放到同一区间内，从而平衡各参数对模型预测结果的影响。
1.2　方法
1.2.1　BP神经网络
随着计算机科学的不断发展，许多智能预测技术相继被提出，其中BPNN是提出较早且发展较为成熟的一种预测模型，由于该模型具有良好的非线性映射能力[18]，可以处理肥胖等级各个影响因素之间的复杂关系。因此，本研究利用其处理非线性关系方面的优势，通过BPNN反向传播机制不断调整网络节点之间的权重[19]，进而捕捉肥胖的诸多影响因素之间的复杂关系，提高预测性能，最终建立BPNN肥胖等级预测模型。
1.2.2　模型优化
由于本研究建立的BPNN肥胖等级预测模型的初始权重和偏置通常以随机方式设定，不恰当的选择会导致模型陷入局部极值而无法实现全局最优[20]。鉴于此，本研究利用遗传算法的全局寻优能力[21-22]，通过交叉和变异等操作跳出局部极值，同时利用模拟退火算法优良的局部搜索能力，使其与遗传算法相结合，使得肥胖等级预测模型既具有良好的全局控制能力，又具有理想的局部搜索能力，可以有效解决肥胖评估这类模型的复杂和非线性问题[23]，全面提升优化效果，进一步提高肥胖等级预测模型的预测精度。
1.2.3　方法流程
本研究主要分为三个部分，首先通过查阅相关资料，找出了2 000多组与肥胖等级有关参数，包括身高、体重等8个参数，并利用皮尔逊相关系数分析方法对各参数的相关性进行了计算，分析了各个参数的重要程度，同时利用归一化方法对肥胖等级相关参数进行了归一化处理，消除了参数间量纲差对模型性能的影响。进一步地，本研究结合肥胖参数维数等具体特征，结合经验公式，通过试算法确定了预测模型的结构，包括隐层节点数和激活函数。最后，研究利用遗传算法以及模拟退火算法对模型参数进行了精确调优，调整了肥胖等级各个参数的权重以及网络节点间的权重，最终建立了GASA-BP肥胖等级预测模型，其流程如图1所示。
1.2.4　模型评价方法
本研究选用了两种评价指标来综合评价模型预测效果，分别为相关系数（R2）和平均绝对偏差（MAE），相关的计算公式[24-25]如下：
式中，yi为实际值；yi′为对应的预测值；n为样本个数。
2　结果
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2.1　变量重要度分析
本研究利用相关性分析对所有参数之间的相关性进行了计算分析，见图2。
由图2可知，与BMI呈正相关的参数由小到大依次为吸烟、NCP、CH2O、身高、CALC、SCC、FCVC、FAVC、年龄、FH、体重，其中相关性最强的是体重，达到了0.913，家族中是否有超重成员相关性也比较强，为0.505。CAEC、FAF、TUE、性别、MTRANS与BMI呈负相关，另外与吸烟相关性最小。通过上述分析可知，体重是影响肥胖等级的最主要因素，因此利用此模型进行预测时要精确测量体重。
2.2　模型结构确定
2.2.1　隐层节点数确定
隐层节点数对于模型预测性能较为重要，当隐含层节点数量不足时，模型可能缺乏足够的鲁棒性，无法充分捕捉输入与输出参数间的复杂关系。当隐含层中包含过多的节点时，这不仅会延长模型预测所需的时间并增加计算代价，还可能引起过拟合的现象，减少模型的普适性。为此，本研究依据经验公式（公式（3））[26]来确定合适的隐含层节点数范围。
式中，n表示输入层对应的节点数；m表示输出层对应的节点数；a为1到10之间的整数。
在本研究中，输入输出参数共17个，对应隐层节点的适宜数量介于5至16之间。基于此，本研究运用减枝法，对每一种可能的隐含层节点数量进行了模型构建，并针对每个模型计算了误差和相关系数。计算结果见图3。当隐藏层的神经元个数被配置为14个时，网络表性能最佳，因此本研究将模型隐层节点数设置为14。
2.2.2　传递函数选择
传递函数与神经网络模型的预测能力也密切相关[27]。在本研究中选取了三种不同的传递函数—purelin、tansig和logsig，用以构建相应的神经网络预测模型，并计算了误差和相关系数，见表3。
由表可知，当隐含层采用“purelin”传递函数时，模型展现出了最佳的预测性能，因此本研究将“purelin”作为传递函数。
2.3　模型预测结果
在本研究中利用相关数据对预测模型进行了训练和预测，见图4。在训练和测试两个阶段中，尽管少数数据点之间存在偏差，大部分数据呈现出一致性，表明提出的预测模型具备出色的适应性和泛化能力，有能力对新的数据进行有效的预测分析。
2.4　不同预测模型预测效果对比
为了对MPGA-BP神经网络预测模型的精确度进行有效评估，本研究建立了极限学习机（ELM），BP神经网络以及GA-BP神经网络预测模型作为对比模型，并计算了误差和相关系数，结果见图5和表4。
基于预测准确度的评估，模型的优先级可以排列为：GASA-BP神经网络、GA-BP神经网络、BP神经网络以及极限学习机。在预测模型的性能比较中，ELM的预测效果不如神经网络模型，这表明神经网络在肥胖等级预测方面具有较好的适用性。
3　讨论
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造成肥胖的原因较多且复杂，除了日常饮食外还有遗传、运动等诸多因素，若不加以控制会对生活产生较大影响甚至会损害身体健康，因此针对个人情况进行肥胖等级预测，进而采取相应的措施对于提高生活质量至关重要。
近年来数据挖掘技术在肥胖研究领域应用广泛，Osadchiy[28]使用机器学习的方法来利用大量的微观结构神经成像和粪便代谢组学数据，以更好地理解与超重表型相比，肥胖的关键驱动因素；张鑫等[29]通过聚类分析等方法，比较分析了南北方民族腰围身高比值等参数的差异，为判定中心性肥胖及相关疾病提供科学参考。可以看出机器学习可以对与肥胖有关的领域进行精准预测，为相应的预防及管理措施提供一定的指导。
虽然机器学习方法在肥胖领域的研究很多[30]，但是通过个人的生活习惯以及家族历史等对自身的肥胖情况进行预测的研究还比较少。对此，本研究通过查阅相关资料，通过UCI数据库收集到2 111组相关数据，并通过相关性分析对数据重要性进行了分析。基于这些数据建立了基于机器学习肥胖等级预测模型，对模型结构进行了优选，发现当BP神经网络隐层节点数为14，传递函数为“purelin”时效果最佳。另外，利用遗传算法以及模拟退火算法对模型进行优化，最终建立了GASA-BP神经网络肥胖等级预测模型，结果显示相较于极限学习机等预测模型，该方法预测精度最高，可对肥胖等级进行精准预测。相较于BP神经网络和GA-BP神经网络预测模型，GASA-BP模型实现了最低的预测偏差，并且预测结果更为接近真实值，这证实了GASA-BP模型在预测精度上的优越性。这种结合遗传算法和模拟退火算法的策略，不仅起到了优化BP神经网络权重和阈值的关键作用，同时在一定程度上缓解了遗传算法出现的早熟收敛问题，进一步增强了模型的预测精度。
本研究利用与BMI呈正相关的参数由小到大依次为吸烟、NCP、CH2O、身高、CALC、SCC、FCVC、FAVC、年龄、FH、体重，其中相关性最强的是体重，达到了0.913，家族中是否有超重成员相关性也比较强，为0.505，CAEC、FAF、TUE、性别、MTRANS与BMI呈负相关。通过上述分析可知，体重是影响肥胖等级的最主要因素，因此在日常生活中需要时刻关注体重变化，利用此模型进行预测时也要精确测量体重。同时家族是否有超重成员以及高热量食物摄入等因素对肥胖也有较大影响，因此需要关注个人生活的各个方面，全面系统地进行分析。
综上所述，影响肥胖的因素较多且复杂，对其进行评估需要考虑许多因素，利用GASA-BP模型对个体特征进行全面分析进而对肥胖等级进行预测具有更强的优势，可以为肥胖预防措施的制定提供一定的借鉴，为精准医疗提供理论支持。但本文也存在一定的局限性，获取的数据只来自部分地区，后期在条件许可的情况下可收集其他地区的数据对模型进行训练，进一步提升适应范围。
基金
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  • 教育部人文社会科学研究一般项目(23YJAZH145)
  • 广西社会医学与卫生事业管理学“八桂学者”
文献
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2024年第51卷第18期
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文章信息
doi: 10.20043/j.cnki.MPM.202404549
  • 接收时间：2024-04-30
  • 首发时间：2026-03-20
  • 出版时间：2024-09-25
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  • 收稿日期：2024-04-30
基金
教育部人文社会科学研究一般项目(23YJAZH145)
广西社会医学与卫生事业管理学“八桂学者”
作者信息
    1.桂林医学院人文与管理学院，广西 桂林 541000
    2.青岛市市立医院病案管理科

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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
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红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
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